I just Googled "rhetoric." The top search result defines rhetoric as "the art of effective or persuasive speaking or writing, especially the use of figures of speech and other compositional techniques." Rhetoric is one of those topics that is fundamental to our society, and one of three ancient arts of discourse. But, something interesting about rhetoric has been going on for the last half-century, and modeling and simulation (M&S) is at the core of the excitement. A quick diversion to Syria and Palmyra with Wired Magazine's article entitled "A Jailed Activist's 3-D Models could save Syria History from ISIS." Bassel Khartabil created 3-D models of the ancient ruins of Palmyra and is currently jailed in Syria. There is a group of online "activists, archivists, and archaeologists" releasing 3D models under the name The New Palmyra Project. First, this is a welcome project and a great humanitarian cause. Second, New Palmyra is an example of how rhetoric has been changing in the digital age. Rhetoric is no longer limited to videos, photographs, and written texts. Models, in the form of models of geometry and dynamics, represent the new rhetorical force. If you want strength in your argument, you rely on models. The Climate Change 2014 Synthesis Report Summary for Policymakers is based on multiple models of climate. While data charts are interesting, it is what is hidden behind the data that is even more interesting: models of how climate changes, with its many effects (e.g., flooding, wind damage). Speaking as a member of the modeling and simulation discipline, we need to embrace modeling with a capital "M", meaning models of information, dynamics, and 3D models like those initiated by Bassel Khartabil.
I started playing chess at a young age when my uncle in England sent me a tiny plastic chess set for Christmas. What were these strange pieces? How did they move? Before long, I learned that they could make interesting patterns on the checkered board. I followed Fischer vs. Spassky with an almost religious fervor. Over time, I became interested in computer science and followed those who made chess machines and software. And then came the inevitable day when the machine beat the reigning world champion (Kasparov). What were we to do now? I guess there goes chess out the window. But no. Humans continued to play chess, and the game is as popular, or more, than ever. There a lesson here. Just because we teach machines to excel at artificial intelligence and at machine learning doesn't mean we stop our quest for life-long learning and enjoyment. Big data is hot. The machine can run through an array of sophisticated algorithms so that, for instance, your search engine experience is more meaningful. I am grateful for this capability and the research that goes into it. Think of the massively complex data networks and automated inferences and patterns generated from them. And yet, I find myself interested in teaching students to draw small networks for things that they see around them. By doing this, students learn something about semantic networks and concept maps (ideas developed by artificial intelligence researchers in the 1970s). The learning that occurs is personal and in this case, does not require the big. It requires an attention to detail and a never-ending fascination with discovery.
This is the start of my life in programming. 10th Grade. Downingtown High School in Pennsylvania. My father told me of an after-school class being taught by two teachers from a local college-- West Chester State College as it was known at the time (1970). In my first class, I was provided with a blue/green IBM plastic programming template. I think the first assignment was to write a program that read a count (N) of numbers, averaged these numbers, and then output the average of the numbers. We had a card punch machine in the school, and our card decks were shipped to the College for processing. Later, we received our green-striped paper output. It was a long process. Although some may see programming as a sort of automation, I was creating a new "machine" with each program. Imagine being told that you could create a machine, but that you'd need to "write" a machine rather than build one. This is the essence of programming. Even today, a program is a mental model of an ethereal machine--stuff that moves around, is stored, and manipulated. Writing doesn't really do the "machine" justice. It is really all visual, a mental model, and I used writing because that is all that was available. Today, coders and modelers do the same thing -- they create imaginary machines that are not quite real--all existing in the head, but real enough to reason about unless too many weeks have passed, and the formerly crisp memory of the machine begins to decay. The goal of programming and modeling is not to automate a task but to understand one. What better way of understanding something than to design a machine to recreate that thing? Want to know how to bake an apple pie? Write a program. Create an apple pie machine. As the old saying goes, if you cannot write a program (or make a machine) then you don't understand the task sufficiently.
I recently engaged in a three-way podcast conversation covering research that we do in the CA lab, as well as activities in the Creative Automata class that I teach--if that is even the right word. Guide? The title of this post is gleaned from Christopher White who works with Elecia White. I engaged in dialogue with both of them, and thoroughly enjoyed our discussion. Elecia and Chris produce a podcast called Embedded where the main theme is embedded systems and electronics. But they tackle a wide variety of interesting topics around this central theme. This audio podcast name Bubblesort Yourself was invented by Elecia, and the hour long podcast can be found here. Their Embedded podcast can also be accessed using the Apple podcast app or the equivalent app on Android phones and tablets. I listen to their podcasts regularly, and also to other podcasts while I take long walks. For some of you, driving the car or working out in the gym may be good times for podcast listening. Chris White also posted an accompanying blog entry where he expands upon formalized synesthesia. Is that what we do when we model in simulation? It seems to be on the basis that we employ many models, each of which contains a hidden set of analogies. The models are encoded with respect to our senses [credit: artwork Synesthesia above is from Nuno de Matox].
If you go to Google Images, and you type in the word "modeling," if you are like me, you might expect to see all sorts of equations, diagrams, and software interfaces allowing scientists and engineers to model complicated things. Instead, from the public's perspective, or perhaps from the perspective of the advertising industry, modeling means to model clothing. Fashion. Runways. The above image is from Top Ten Modeling Agencies. At first glance, this type of modeling is something we might be tempted to dismiss as irrelevant to our supposedly higher ambitions within mathematics, science, and engineering. But, this type of modeling is ubiquitous and serves as a good way to talk about modeling to others. Why not talk about models by covering fashion? You probably have heard of a "model house" or a "model kitchen." This type of model refers to model as prototype. Rather than modeling a pre-existing phenomenon like automobile traffic or heat exchange, model as prototype brings in design as a type of modeling. The model house may be one you'd like to live in. The model kitchen gives you ideas of how you'd like your kitchen to look and function. So, if you want to explain what you do as a modeler to others, begin with common terms and experiences. Meet people on their ground, with their understanding of model. And wear something suitable like a well-designed pair of jeans. You might be surprised when the next day, your friend shows up wearing the same clothing. You have become a model. [Source concept: from a column that I did long ago for the Society for Modeling & Simulation International].
In a recent audio podcast, three of us were discussing personalized modeling from different angles--including using art and craft-inspiration, and engineering culture. Karen Doore, Sharon Hewitt, and I engaged in a short conversation that is part of a series of podcasts called Creative Disturbance. Anyone who has been to a modeling and simulation conference notices that...the people attending are all quite different. Often having different degrees and from different departments and schools. There is good reason for this: modeling is inherently an area that connects different people and things together. This diversity plays out, also, in our modeling choices. What is your favorite modeling system or language? What underlying analogies are used?
Much of what we do in the Creative Automata (CA) Lab is oriented around multiple representations of a single abstract mathematical concept--such as integration in calculus or sorting in computer science. How can we personalize approaches for learning something like integration? Is it possible to leverage our multiple cultures to engage and motivate the learner? The lab just submitted our video entry to the National Academy of Engineering (NAE) Grand Challenges for Engineering Video Contest called E4U2. Sharon Hewitt from the CA Lab designed and produced this video. The video segments include representations of a virtual analog computer based on the sand-like flow in PowderToy, as well as several personalized models of the Lotka Volterra model. Instead of making models for other people, consider that you can learn about modeling by making these wonders for yourself. In this arts-based approach, you will also interest other people in modeling.
This is a circuit created by a Creative Automata Lab research assistant David Vega. The circuit is a physical incarnation of the Fibonacci difference equation f(m) = f(m-1) + f(m-2), where "m" equals the current month in decimal. We begin the Fibonacci sequence by iteratively solving, beginning with f(0)=f(1)=1. These values jump start the difference equation: f(2) = f(1) + f(0), f(3) = f(2) + f(1), and so forth. The sequence ends up as 1,1,2,3,5,8,13,21,34 until we decide to stop. These numbers are termed Fibonacci numbers. There are all kinds of interesting real-world patterns related to these numbers, including the spiral pattern found within a nautilus shell. This equation represents an idealized model of rabbit population growth. The equation was re-represented as a visual Max/MSP patch, and then translated into an equivalent electronic circuit using Teensy 3.1 microcontroller boards. The boards, populating the breadboard above, are connected using serial communications, and there is "software clock" that regulates the data flow. In Max/MSP this clock is programmed as a [metro] (short for metronome) object. This circuit will be transformed yet again into a tangible artwork where the Teensy boards are housed in 3d printed rabbit objects. I'll post another entry when we get to that stage. You've probably heard of "embedded systems," so this is a case where the embedding is meant to draw in the participant in a way not really possible with the textual difference equation.
When I landed my first academic job at the University of Florida in the mid-80s, I began a slow and steady life journey of knowledge enrichment, which included making friends and colleagues in different schools. One of the things that confused me then, and continues to be a puzzle now, is the schism existing between and among areas such as arts, humanities, science, and engineering. Here is one example of a schism, or perhaps more of a deep canyon. Recall that the humanities are traditionally very old subjects--been around a while and dominated by reading and writing: Scholarly production. Then, consider the arts, and by "arts," I am referring to the arts of the senses such as fine art, ceramics, sculpture, and performing arts such as theatre and dance. Tasks that involve making live in the art building. People are making things--paintings, kinetic sculptures, cinema. Tasks that involve writing are somewhere else on campus--in the humanities building. There is a deep schizophrenia where the people who "make" and the people who "write" don't talk much with each other. As an engineer, I find this peculiar because in engineering, not only are writing and making in the same place, they are also in the same person. All engineers are expected to form carefully worded arguments about what they contribute to knowledge through making; engineering has a high degree of scholarship as do most areas within the university. All of this causes me to wonder whether what is going on in "digital humanities" is actually a leaking of engineering culture into the humanities. Is that a bad thing? I don't think so, and let's not muddy the waters in the digital humanities with misleading phrases like "using a tool" or "using technology." These have nothing to do with what is occurring at a fundamental, philosophical level within the humanities. At the core, the transitions are about a social and cultural osmosis from science and engineering. Similarly, there are big shifts--rooted in the arts and humanities--occurring in science and engineering, but that is the subject of another post.
About a year ago, two of my colleagues (Bonnie Pitman and Cassini Nazir) and I got together and decided to connect. The idea was to connect ideas using Liz Larner's sculpture, appropriately entitled "X". Larner's sculpture was first modeled in wood (above) and then resculpted in steel. The sculpture was on loan to us in the Art & Technology (ATEC) building, and is now heading back to its home at the Nasher Sculpture Center in Dallas. The three of us imagined a web presence and asked everyone to make connections to "X". Making these connections creates bridges across the university, poking holes into the vertical silos defined by colleges, schools, and departments. These connections are part of an online exhibit of perspectives on, and views of, the object. The ideas of modeling, found within the simulation and modeling field, are found in these perspectives. Models are perspectives on a thing: abstracting out space and time. These ideas were brought home for me yesterday when visiting the Brooklyn Museum where they have an exhibit Connecting Cultures. Museums are places where we are encouraged to make connections among people, places, and things. Models are language-based artifacts that assist us with forging new cultures around ways of modeling. So, the worlds of museums, sculptures, and scientific models may have stronger connections than we might think.
Physicality is defined by Merriam-Webster as "intensely physical orientation : predominance of the physical usually at the expense of the mental, spiritual, or social." The above photograph is from a post within a list of physical visualizations. The list begins with the corporeal concept of number in the form of Mesopotamian clay tokens from 5500BC. Included are many fascinating realizations such as a "sculpture" from the Tohoku Japanese earthquake data. The photo from 1985 of Rick Becker of AT&T Bell Labs is particularly striking because on the left, we have a tangible representation, and on the right, something flat on a green screen. To be sure, the screen representation had the advantage of being dynamic and more efficiently constructed. At the same time, something was lost in the evolutionary process-- the physicality disappeared. We may need to revisit the past to see what can be reconstructed, but with a new flair guided by technologies such as 3D printing and embedded micro controller-based systems. We may also need to evolve our culture because we have lived in flatland for so long. [Related post The Disappearing Trick of HiTech]